Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Schematic drawings of route directions are one of the most common forms of graphic communication. People make sketches to communicate geographical ideas, and professionally designed schematic maps give orientation to thousands of users of a public transport system. Creating a schematic map for representing a transport network may be seen as a straightforward task; however, the underlying design of such maps can be quite complex. Map designers apply, consciously or subconsciously, various cartographic generalization techniques to emphasize important information and to improve the clarity of map content. At present, traditional mapping and GIS literature offers very little guidance to a map designer seeking cartographic rules or practical ideas for representing the elaborate route data of public transport systems schematically. This article aims to contribute to the design challenges of schematic, route-based mapping. Information about schematic maps and symbolization of route-based data is given. A case study of schematic map design for a public transport network is presented to show the need for support of cartographic science for the creation of schematic transport maps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it